Although uncommon today, the rapid progress being made in Machine Learning (ML) and Natural Language Processing is bringing within reach of the telecommunications industry the day when computer systems will be able to engage in conversation with humans to achieve an objective. This is best illustrated by the progress being made with today's mobile assistants such as for example, Alexa, Google Home, Siri and Cortana. The current state of the art is such that these systems are generally limited to simple request/responses. However, recent progress (most notable Google Duplex and Alexa “multi turn dialog”), extends this to the point where for short conversations the human participant may not be aware the other party is an Artificial Intelligence (AI).
This shift in abilities, introduces several implications to various industries and in particular to the telecommunications industry. Many of these abilities are advantageous, but there are some which require changes to the network to prevent abuse of systems and/or to optimize system resources. Key to preventing abuse of systems and/or optimizing system resources in such instances will be the ability to detect calls which are generated and/or controlled by artificial intelligence systems.
In order to further expound upon the need and/or desire for the ability to detect artificial intelligence calls, three exemplary scenarios in which the detection of artificial intelligence generated and/or controlled calls will now be discussed.
The first scenario relates to a new generation of robocalls whose implementation is anticipated. With the advancement of artificial intelligence and the ability to interact with humans in conversation, it is anticipated that a new generation of robocalls will also evolve beyond the simple “dial and play announcement/interactive voice response-script” style robocalls of today. The more natural engagement of artificial intelligence systems will result in end users being less aware a call is a robocall immediately, possibly changing call hold times, and making it less obvious to certain detection systems that robocalls are being received.
The second scenario relates to nuisance calls. It is not a given that these AI systems will always work effectively, and it is highly probable that some end systems (e.g., businesses) will consider these AI systems a source of nuisance calls. However, these AI calls can be highly distributed (e.g., being sourced from any arbitrary mobile device, e.g., smartphones, cellphone, tablet, etc.) and therein harder to screen based on source identifiers.
The third scenario relates to call handling. It is anticipated that enterprises and individuals receiving an inbound call will want the ability to treat calls generated by/received from an AI differently to calls from a human while no solution to this anticipated need currently exists.
From the foregoing it is apparent that there is a need for a technological solution to how to effectively, efficiently and in a cost-efficient manner identify artificial intelligence calls. Furthermore, there is a need for a technical solution to the problem of how to optimize resources in connection with artificial intelligence calls. There is also a need for interworking of an “AI indication” between different signalling and media encoding schemes to aid compatibility in complex networks.